Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
DeMo: Decoupling Motion Forecasting into Directional Intentions and Dynamic States
Authors: Bozhou Zhang, Nan Song, Li Zhang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on both the Argoverse 2 and nu Scenes benchmarks demonstrate that our De Mo achieves state-of-the-art performance in motion forecasting. |
| Researcher Affiliation | Academia | Bozhou Zhang Nan Song Li Zhang School of Data Science, Fudan University |
| Pseudocode | No | No pseudocode or algorithm blocks were found. |
| Open Source Code | Yes | https://github.com/fudan-zvg/De Mo |
| Open Datasets | Yes | We evaluate our method s performance using the Argoverse 2 [67] and nu Scenes [3] motion forecasting datasets. |
| Dataset Splits | Yes | Ablation study on the core components of De Mo on the Argoverse 2 single-agent validation set. |
| Hardware Specification | Yes | All experiments are conducted on 8 NVIDIA Ge Force RTX 3090 GPUs. |
| Software Dependencies | No | The paper mentions software components like Adam W optimizer, nn.Layer Norm, and nn.GELU, but does not provide specific version numbers for these or any underlying libraries (e.g., PyTorch, TensorFlow). |
| Experiment Setup | Yes | Our models are trained for 60 epochs using the Adam W [42] optimizer, with a batch size of 16 per GPU. The training is conducted end-to-end with a learning rate of 0.003 and a weight decay of 0.01. |